Society increasingly demands accurate predictions of complex ecosystem processes under novel conditions to address environmental challenges. However, obtaining the process-level knowledge required to do so does not necessarily align with the burgeoning use in ecology of correlative model selection criteria, such as Akaike information criterion. These criteria select models based on their ability to reproduce outcomes, not on their ability to accurately represent causal effects. Causal understanding does not require matching outcomes, but rather involves identifying model forms and parameter values that accurately describe processes. We contend that researchers can arrive at incorrect conclusions about cause-and-effect relationships by relying on information criteria. We illustrate via a specific example that inference extending beyond prediction into causality can be seriously misled by information-theoretic evidence. Finally, we identify a solution space to bridge the gap between the correlative inference provided by model selection criteria and a process-based understanding of ecological systems.

Soil organic matter (SOM) stocks, decomposition and persistence are largely the product of controls that act locally. Yet the controls are shaped and interact at multiple spatiotemporal scales, from which macrosystem patterns in SOM emerge. Theory on SOM turnover recognizes the resulting spatial and temporal conditionality in the effect sizes of controls that play out across macrosystems, and couples them through evolutionary and community assembly processes. For example, climate history shapes plant functional traits, which in turn interact with contemporary climate to influence SOM dynamics. Selection and assembly also shape the functional traits of soil decomposer communities, but it is less clear how in turn these traits influence temporal macrosystem patterns in SOM turnover. Here, we review evidence that establishes the expectation that selection and assembly should generate decomposer communities across macrosystems that have distinct functional effects on SOM dynamics. Representation of this knowledge in soil biogeochemical models affects the magnitude and direction of projected SOM responses under global change. Yet there is high uncertainty and low confidence in these projections. To address these issues, we make the case that a coordinated set of empirical practices are required which necessitate (1) greater use of statistical approaches in biogeochemistry that are suited to causative inference; (2) long-term, macrosystem-scale, observational and experimental networks to reveal conditionality in effect sizes, and embedded correlation, in controls on SOM turnover; and (3) use of multiple measurement grains to capture local- and macroscale variation in controls and outcomes, to avoid obscuring causative understanding through data aggregation. When employed together, along with process-based models to synthesize knowledge and guide further empirical work, we believe these practices will rapidly advance understanding of microbial controls on SOM and improve carbon cycle projections that guide policies on climate adaptation and mitigation.

We develop a demographic approach for estimating the utility discount rate (UDR) portion of the Ramsey rule. We show how age-specific mortality rates and life expectancies imply a natural, long-term UDR for individuals at each age, and these can be aggregated into a population-level social UDR. We provide estimates for nearly all countries and the world. Our estimates fall within the range of those currently employed in the literature, and the empirical basis of our methodology provides a useful point of comparison for alternative assumptions about the UDR. We use our results to derive heterogeneous social discount rates across countries and explore the consequences for an integrated assessment model of climate change. We find that introducing regional heterogeneity of UDRs into the RICE model has a small effect on the business-as-usual trajectory of global emissions, yet a more substantial effect on optimal emissions and the distributional burden of emission reductions.

Sustainable development of the ocean economy requires a system for measuring progress. The standard system of national accounting provides a solid foundation for doing so, though the scope requires expansion to adequately cover household-produced services; for example, ocean-based leisure, and the role of natural capital in the ocean economy. The accounts summary needs indicators beyond gross domestic product that enable users to choose what is included. New technologies make digital dashboards of indicators easy to produce. Such dashboards facilitate rapid comparison of indicators that cannot be aggregated into a single metric, which enables national ocean accounts to coherently present physical and monetary data.

Many fishers own a portfolio of permits across multiple fisheries, creating an opportunity for fishing effort to adjust across fisheries and enabling impacts from a policy change in one fishery to spill over into other fisheries. In regions with a large and diverse number of permits and fisheries, joint-permitting can result in a complex system, making it difficult to understand the potential for cross-fishery substitution. In this study, we construct a network representation of permit ownership to characterize interconnectedness among Alaska commercial fisheries due to cross-fishery permitting. The Alaska fisheries network is highly connected, suggesting that most fisheries are vulnerable to cross-fishery spillovers from network shocks, such as changes to policies or fish stocks. We find that fisheries with similar geographic proximity are more likely to be a part of a highly connected cluster of susceptible fisheries. We use a case study to show that preexisting network statistics can be useful for identifying the potential scope of policy-induced spillovers. Our results demonstrate that network analysis can improve our understanding of the potential for policy-induced cross-fishery spillovers.

Location matters for the value of capital assets. The value of changes in natural capital wealth can depend on whether natural capital asset prices are measured locally and then aggregated or whether average values are applied over aggregate representative areas. Spatial heterogeneity of resource characteristics and institutions impact approximations of the intertemporal welfare function and accounting price function because when spatial aggregation precedes valuation it implies greater arbitrage opportunities leading to more inelastic shadow (accounting) price functions than when valuation is done locally and then aggregated. Aggregation of observed values across varying resource and institutional characteristics can lead to omitted variables bias. We illustrate these results in the context of groundwater in the Kansas High Plains Aquifer and demonstrate that the accounting price function is less elastic when the accounting price is measured locally. Failure to measure locally and then aggregate could lead to undervaluing scarce resources and overvaluing plentiful ones, which biases wealth accounts in favor of passing the non-declining wealth sustainability test.

We present a novel algorithm for the design of crossing experiments. The algorithm identifies a set of individuals (a 'crossing-set') from a larger pool of potential crossing-sets by maximizing the diversity of traits of interest, for example, maximizing the range of genetic and geographic distances between individuals included in the crossing-set. To calculate diversity, we use the mean nearest neighbor distance of crosses plotted in trait space. We implement our algorithm on a real dataset of Neurospora crassa strains, using the genetic and geographic distances between potential crosses as a two-dimensional trait space. In simulated mating experiments, crossing-sets selected by our algorithm provide better estimates of underlying parameter values than randomly chosen crossing-sets.